The Horizon Effect, also known as the horizon problem, is caused by the depth limitation of the search algorithm.

• Because only a partial game tree has been analyzed, it will appear to the system that an event can be avoided when in fact this is not the case.
• Besides obligatory Quiescence search, Extensions, especially Check Extensions are designed to reduce horizon effects.

When evaluating a large game tree using techniques such as Minimax with Alpha-Beta pruning, search depth is limited for feasibility reasons.

• However, evaluating a partial tree may give a misleading result.
• When a significant change exists just over the horizon of the search depth, the computational device falls victim to the horizon effect.

Greedy algorithms tend to suffer from the horizon effect.

The horizon effect can be mitigated by extending the search algorithm with a quiescence search.

• This gives the search algorithm ability to look beyond its horizon for a certain class of moves of major importance to the game state, such as captures in chess.

Rewriting the evaluation function for leaf nodes and/or analyzing more nodes will solve many horizon effect problems.

Horizon Effect